1. [1] M. Khaneiki, "Territorial Water Cooperation in the Central Plateau of Iran", Springer Nature, Switzerland, 2019.

2. [2] A. Yazdi and M. Khaneiki, "Qanat Knowledge: construction and maintenance", Springer, Dordrecht, 2017.

3. [3] H. Drucker, C. J. Burges, L. Kauffman, A. Smola and V. Vapnik, "Support vector regression machines. Neural information processing systems", Eds Mozer MC, Jordan JI & Petsche T. pp. 155-161, 1997.

4. [4] S. Watanabe, K. Sumi and T. Ise, "Identifying the vegetation type in Google Earth images using a convolutional neural network: a case study for Japanese bamboo forests", BMC Ecol 20, 65, 2020. [DOI:10.1186/s12898-020-00331-5]

5. [5] Y. Bengio, "Learning deep architectures for AI", Foundat Trends Mach Learn, 2:1-127, 2009. [DOI:10.1561/2200000006]

6. [6] I. Goodfellow, Y. Bengio and A. Courville, "Deep Learning", MIT press, 2016.

7. [7] Y. LeCun, Y. Bengio and G. Hinton, "Deep learning", Nature, vol. 521, pp. 436_444, 2015. [DOI:10.1038/nature14539]

8. [8] A. Voulodimos, N. Doulamis, A. Doulamis and E. Protopapadakis, "Deep learning for computer vision: A brief review, Comput. Intell", Neurosci., vol. 2018, pp. 1_13, 2018. [DOI:10.1155/2018/7068349]

9. [9] L. Deng, G. Hinton and B. Kingsbury, "New types of deep neural network learning for speech recognition and related applications: An overview", in Proc. IEEE Int. Conf. Acoust., Speech Signal Process., pp. 8599_8603, 2013. [DOI:10.1109/ICASSP.2013.6639344]

10. [10] H. Palangi, L. Deng, Y. Shen, J. Gao, X. He, J. Chen, X. Song and R. Ward, "Deep sentence embedding using long short-term memory networks: Analysis and application to information retrieval", IEEE/ACM Trans, Audio, Speech, Language Process., vol. 24, no. 4, pp. 694_707, Apr, 2016. [DOI:10.1109/TASLP.2016.2520371]

11. [11] A. Karpathy, G. Toderici, S. Shetty, T. Leung, R. Sukthankar and L. Fei-Fei, "Largescale video classification with convolutional neural networks", Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (Columbus). 1725-1732, 2014. [DOI:10.1109/CVPR.2014.223]

12. [12] S. Alberto, M. Savardi, A. Baronio and S. Benini, "Deep learning meets hyperspectral image analysis: A multidisciplinary review", Journal of Imaging 5, no. 5: 52, 2019. [DOI:10.3390/jimaging5050052]

13. [13] K. Nogueira, O.A.B. Penatti and J.A. Dos Santos, "Towards better exploiting convolutional neural networks for remote sensing scene classification", Pattern Recogn. 61:539-56, 2017. [DOI:10.1016/j.patcog.2016.07.001]

14. [14] X.X. Zhu, D. Tuia, L. Mou, G.S. Xia, L. Zhang and F. Xu, "Deep Learning in remote sensing: a comprehensive review and list of resources", IEEE Geosci Remote Sens Mag. 5:8-36, 2017. [DOI:10.1109/MGRS.2017.2762307]

15. [15] L. Ma, Y. Liu, X. Zhang, Y. Ye, G. Yin and B. A. Johnson, "Deep learning in remote sensing applications: A meta-analysis and review", ISPRS J. Photogram. Remote Sens. 152, 166-177, 2019. [DOI:10.1016/j.isprsjprs.2019.04.015]

16. [16] S. Mehrnoush, A. Mehrtash, E. Khazraee and A. Jason, "Deep learning in archaeological remote sensing: Automated qanat detection in the kurdistan region of Iraq", Remote Sensing 12, no. 3: 500, 2020. [DOI:10.3390/rs12030500]

17. [17] Q. Li, H. Guo, L. Luo and X. Wang, "Automatic Mapping of Karez in Turpan Basin Based on Google Earth Images and the YOLOv5 Model", Remote Sens, 14, 3318, 2022. [DOI:10.3390/rs14143318]

18. [18] D. Erhan, C. Szegedy, S. Reed, C.Y. Fu and A.C. Berg, "SSD: Single shot multibox detector", In Proceedings of the Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Springer: Cham, Switzerland, Volume 9905, pp. 21-37, 2016. [DOI:10.1007/978-3-319-46448-0_2]

19. [19] K. He, X. Zhang, S. Ren and J. Sun, "Deep Residual Learning for Image Recognition", in CVPR, 2016. [DOI:10.1109/CVPR.2016.90]

20. [20] B. Xu, N. Wang, T. Chen and M. Li, "Empirical evaluation of rectified activations in convolutional network", arXiv Prepr. arXiv:00853/1505, 2015.

21. [21] W. Liu, D. Anguelov, D. Erhan and et al., "SSD: single shot multibox detector", In: European conference on computer vision. Cham: Springer, p. 21-37, 2016. [DOI:10.1007/978-3-319-46448-0_2]

22. [22] K. He, X. Zhang, S. Ren and J. Sun, "Deep Residual Learning for Image Recognition", In: arXiv e-prints, 03385/1512, 2015. [DOI:10.1109/CVPR.2016.90]

23. [23] T.Y. Lin, M. Maire, S. Belongie, J. Hays, P. Perona, D. Ramanan, P. Doll'ar and C.L. Zitnick, "Microsoft coco: Common objects in context", In European Conference on Computer Vision; Springer: Berlin/Heidelberg, Germany, pp. 740-755, 2014. [DOI:10.1007/978-3-319-10602-1_48]

24. [24] T. Ise, M. Minagawa and M. Onishi, "Classifying 3 moss species by deep learning, using the "chopped picture" method", Open J Ecol. 8:166-73, 2018. [DOI:10.4236/oje.2018.83011]

25. [25] J. Redmon, S. Divvala, R. Girshick and A. Farhadi, "You Only Look Once: Unified, Real-Time Object Detection", arXiv:02640/1506, 2015. [DOI:10.1109/CVPR.2016.91]

26. [26] J. Jeong, H. Park and N. Kwak, "Enhancement of SSD by concatenating feature maps for object detection", In: BMVC'17, 2017. [DOI:10.5244/C.31.76]